This module presents the implementation of Principal Component Analysis (PCA) and Factor Analysis (FA) using the World Development Indicators dataset. The objective of this analysis is to evaluate the suitability of the dataset for dimensionality reduction techniques through several assumption tests, including correlation analysis, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and Bartlett’s Test of Sphericity. After satisfying all required assumptions, PCA is conducted to identify the principal components that explain the majority of variance within the dataset. The results provide insight into the underlying structure of global economic and development indicators.

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